We present a model based on the concept of saturation for small $Q^2$ and small $x$. With only three parameters we achieve a good description of all Deep Inelastic Scattering data below $x=0.01$. This includes a consistent treatment of charm and a successful extrapolation into the photoproduction regime. The same model leads to a roughly constant ratio of diffractive and inclusive cross section.
Tarek Tohme, Massimo Vergassola, Thierry Mora
et al.
Cells integrate signals and make decisions about their future state in short amounts of time. A lot of theoretical effort has gone into asking how to best design gene regulatory circuits that fulfill a given function, yet little is known about the constraints that performing that function in a small amount of time imposes on circuit architectures. Using an optimization framework, we explore the properties of a class of promoter architectures that distinguish small differences in transcription factor concentrations under time constraints. We show that the full temporal trajectory of gene activity allows for faster decisions than its integrated activity represented by the total number of transcribed mRNA. The topology of promoter architectures that allow for rapidly distinguishing low transcription factor concentrations result in a low, shallow, and non cooperative response, while at high concentrations, the response is high and cooperative. In the presence of non-cognate ligands, networks with fast and accurate decision times need not be optimally selective, especially if discrimination is difficult. While optimal networks are generically out of equilibrium, the energy associated with that irreversibility is only modest, and negligible at small concentrations. Instead, our results highlight the crucial role of rate-limiting steps imposed by biophysical constraints.
Bakhta Fedlaoui, Teresa Cosentino, Zeina Al Sayed
et al.
BACKGROUND: Primary aldosteronism is the most common form of secondary hypertension. The most frequent genetic cause of aldosterone-producing adenomas is somatic mutations in the potassium channel KCNJ5. They affect the ion selectivity of the channel, with sodium influx leading to cell membrane depolarization and activation of calcium signaling, the major trigger for aldosterone biosynthesis. METHODS: To investigate how KCNJ5 mutations lead to the development of aldosterone-producing adenomas, we established an adrenocortical cell model in which sodium entry into the cells can be modulated on demand using chemogenetic tools [H295R-S2 $α$7-5HT3-R ($α$7-5HT3 receptor) cells]. We investigated their functional and molecular characteristics with regard to aldosterone biosynthesis and cell proliferation. RESULTS: A clonal cell line with stable expression of the chimeric $α$7-5HT3-R in H295R-S2 (human adrenocortical carcinoma cell line, Strain 2) cells was obtained. Increased sodium entry through $α$7-5HT3-R upon stimulation with uPSEM-817 (uPharmacologically Selective Effector Molecule-817) led to cell membrane depolarization, opening of voltage-gated Ca 2+ channels, and increased intracellular Ca 2+ concentrations, resulting in the stimulation of CYP11B2 expression and increased aldosterone biosynthesis. Increased intracellular sodium influx did not increase proliferation but rather induced apoptosis. RNA sequencing and steroidome analyses revealed unique profiles associated with Na + entry, with only partial overlap with Ang II (angiotensin II) or potassium-induced changes. CONCLUSIONS: H295R-S2 $α$7-5HT3-R cells are a new model reproducing the major features of cells harboring KCNJ5 mutations. Increased expression of CYP11B2 and stimulation of the mineralocorticoid biosynthesis pathway are associated with a decrease of cell proliferation and an increase of apoptosis, indicating that additional events may be required for the development of aldosterone-producing adenomas.
Decision-theoretic planning is a popular approach to sequential decision making problems, because it treats uncertainty in sensing and acting in a principled way. In single-agent frameworks like MDPs and POMDPs, planning can be carried out by resorting to Q-value functions: an optimal Q-value function Q* is computed in a recursive manner by dynamic programming, and then an optimal policy is extracted from Q*. In this paper we study whether similar Q-value functions can be defined for decentralized POMDP models (Dec-POMDPs), and how policies can be extracted from such value functions. We define two forms of the optimal Q-value function for Dec-POMDPs: one that gives a normative description as the Q-value function of an optimal pure joint policy and another one that is sequentially rational and thus gives a recipe for computation. This computation, however, is infeasible for all but the smallest problems. Therefore, we analyze various approximate Q-value functions that allow for efficient computation. We describe how they relate, and we prove that they all provide an upper bound to the optimal Q-value function Q*. Finally, unifying some previous approaches for solving Dec-POMDPs, we describe a family of algorithms for extracting policies from such Q-value functions, and perform an experimental evaluation on existing test problems, including a new firefighting benchmark problem.
The method developed by Michaelis and Menten was foundational in the development of our understanding of biochemical reaction kinetics. Extended models of metabolism encapsulated by reaction rate theory, stochastic reaction models, and dynamic flux estimation, amongst others, address aspects of this fundamental idea. The limitations of these approaches are well understood, and efforts to overcome those issues so far have been plentiful but with limited success. The known issues can be summarised as the sole dependent relation with substrate concentration, the encapsulation of rate in a single relevant scalar, and the subsequent lack of functional control that results from this assumption. The Rate Control of Chaos (RCC) is a nonlinear control method that has been shown to be effective in controlling the dynamic state of biological oscillators based on the concept of rate limitation of the exponential growth in chaotic systems. Extending RCC with allosteric properties allows robust control of the enzymatic process, and replicates the Michaelis-Menten kinetics. The emergent dynamics is robust to perturbations and noise but susceptible to regulatory adjustments. This control method adapts the control parameters dynamically in the presence of a ligand, and permits introduction of energy relations into the control function. The dynamic nature of the control eliminates the steady-state requirements and allows the modelling of large-scale dynamic behaviour, potentially addressing issues in metabolic disorder and failure of metabolic control.
A bstractWe introduce a new framework for constructing black hole solutions that are holographically dual to strongly coupled field theories with explicitly broken translation invariance. Using a classical gravitational theory with a continuous global symmetry leads to constructions that involve solving ODEs instead of PDEs. We study in detail D = 4 Einstein-Maxwell theory coupled to a complex scalar field with a simple mass term. We construct black holes dual to metallic phases which exhibit a Drude-type peak in the optical conductivity, but there is no evidence of an intermediate scaling that has been reported in other holographic lattice constructions. We also construct black holes dual to insulating phases which exhibit a suppression of spectral weight at low frequencies. We show that the model also admits a novel AdS3 × $ \mathbb{R} $ solution.
Neutron Star Measurements Neutron stars are one of the densest manifestations of matter in the universe. Yagi and Yunes (p. 365) examined the moment of inertia of neutron stars, which determines how fast they can spin, and the quadrupole moment and tidal Love number, which determine how much they can be deformed. The findings suggest that these three quantities obey universal relationships that are independent of the internal structure of the stars, implying that measurements of one of the three could accurately predict the other two. The relation of inertia, Love number, and quadrupole moment is independent of neutron and quark stars’ internal structure. Neutron stars and quark stars are not only characterized by their mass and radius but also by how fast they spin, through their moment of inertia, and how much they can be deformed, through their Love number and quadrupole moment. These depend sensitively on the star’s internal structure and thus on unknown nuclear physics. We find universal relations between the moment of inertia, the Love number, and the quadrupole moment that are independent of the neutron and quark star’s internal structure. These can be used to learn about neutron star deformability through observations of the moment of inertia, break degeneracies in gravitational wave detection to measure spin in binary inspirals, distinguish neutron stars from quark stars, and test general relativity in a nuclear structure–independent fashion.
Sara Gushgari-Doyle, Lauren M Lui, Torben N Nielsen
et al.
Abstract Niche environmental conditions influence both the structure and function of microbial communities and the cellular function of individual strains. The terrestrial subsurface is a dynamic and diverse environment that exhibits specific biogeochemical conditions associated with depth, resulting in distinct environmental niches. Here, we present the characterization of seven distinct strains belonging to the genus Arthrobacter isolated from varying depths of a single sediment core and associated groundwater from an adjacent well. We characterized genotype and phenotype of each isolate to connect specific cellular functions and metabolisms to ecotype. Arthrobacter isolates from each ecotype demonstrated functional and genomic capacities specific to their biogeochemical conditions of origin, including laboratory-demonstrated characterization of salinity tolerance and optimal pH, and genes for utilization of carbohydrates and other carbon substrates. Analysis of the Arthrobacter pangenome revealed that it is notably open with a volatile accessory genome compared to previous pangenome studies on other genera, suggesting a high potential for adaptability to environmental niches.
The technical virtual power plant (TVPP) is a promising paradigm to facilitate the integration of distributed energy resources (DERs) while incorporating operational constraints of both DERs and networks. Due to the volatility and limited predictability of DER generation and electric loads, the output capability of the TVPP is uncertain. In this regard, this paper proposes the robust capability curve (RCC) of the TVPP, which explicitly characterizes the allowable range of the scheduled power output that is executable for the TVPP under uncertainties. Implementing the RCC can secure the scheduling of the TVPP against unexpected fluctuations of operating conditions when the TVPP participates in the transmission-level dispatch. Mathematically, the RCC is the first-stage feasible set of an adjustable robust optimization problem. An uncertainty set model incorporating the variable correlation and uncertainty budget is employed, which makes the robustness and conservatism of the RCC adjustable. A novel methodology is proposed to estimate the RCC by the convex hull of several points on its perimeter. These perimeter points are obtained by solving a series of multi scenario-optimal power flow problems with worst-case uncertainty realizations identified based on a linearized network configuration. Case studies based on the IEEE-13 test feeder validate the effectiveness of the RCC to ensure the scheduling feasibility while hedging against uncertainties. The computational efficiency of the proposed RCC estimation method is also verified based on larger-scale test systems.